Marginal Partial Likelihood Approach in the Cox Model with Non-ignorable Missing Covariates

Author

Huanbin, Liu ; Liuquan, Sun

Author_Institution

Sch. of Math. & Stat., Huazhong Univ. of Sci. & Technol., Wuhan, China

fYear

2009

fDate

11-12 July 2009

Firstpage

541

Lastpage

545

Abstract

Marginal partial likelihood approach is used for estimating the parameters for the Cox model with missing covariates and a non-ignorable missing data mechanism. An efficient algorithm based on Markov chain Monte Carlo stochastic approximation is proposed to solve the resulting estimating equations. Simulation studies show that the proposed estimation procedure works well and gives accurate estimates and their variance estimates. We also illustrate the method with a melanoma data set.

Keywords

Markov processes; Monte Carlo methods; approximation theory; maximum likelihood estimation; Cox model; Markov chain Monte Carlo; covariate; estimation procedure; marginal partial likelihood approach; missing data mechanism; stochastic approximation; Approximation algorithms; Automatic control; Control system synthesis; Hazards; Loss measurement; Mathematical model; Mathematics; Maximum likelihood estimation; Parameter estimation; Sampling methods; Cox model; Gibbs sampling; Markov chain Monte Carlo methods; Metroplis-Hastings algorithm; Missing data mechanism; Stochastic approximation;

fLanguage

English

Publisher

ieee

Conference_Titel

Control, Automation and Systems Engineering, 2009. CASE 2009. IITA International Conference on

Conference_Location

Zhangjiajie

Print_ISBN

978-0-7695-3728-3

Type

conf

DOI

10.1109/CASE.2009.58

Filename

5194511